지석호 교수

Knowledge management for construction accident cases can identify dangerous conditions and prevent accidents by controlling risks on-site. However, because accident cases are recorded as unstructured text data, significant time and effort are required to retrieve and analyze the knowledge a user wants. To overcome these limitations, this research proposes a knowledge management system for construction accident cases using natural language processing. For this purpose, two models were developed t

Unsafe acts of workers (e.g., misjudgment or inappropriate operation) become the major root causes of construction accidents when they are combined with unsafe working conditions (e.g., work surface conditions or weather) on a construction site. The overarching goal of the research presented in this paper is to explore ways to prevent unsafe acts of workers and reduce the likelihood of construction accidents occurring. The study specifically aims to (1) understand the relationships between human

Activity identification is an essential step to measure and monitor the performance of earthmoving operations. Many vision-based methods that automatically capture and explain activity information from image data have been developed with economic advantages and analysis efficiency. However, the previous methods failed to consider the interactive operations among equipment, and thus limited the applicability to the operation time estimation for productivity analysis. To address the drawback, this

This paper presents an automated image-based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image-based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules fo

To be successful in managing earthmoving projects, it is very important to monitor the operational efficiency and productivity of heavy equipment. Researchers have investigated many vision-based methods and demonstrated their high applicability to automated productivity monitoring. However, they primarily focused on developing a single-camera vision-based approach that monitors heavy equipment's movement using video data collected from only one camera, and thus they normally failed in continuous

Detecting contractual risk information from construction specifications is crucial to succeeding in construction projects. This paper describes clause classification using the Bidirectional Encoder Representations from Transformers (BERT) method in natural language processing. Seven risk categories are determined from a literature review, including payment, temporal, procedure, safety, role and responsibility, definition, and reference. Using 2807 clauses from 56 construction specifications, the

When bidding on construction projects, contractors need to understand the specifications properly to manage project risks. However, specifications are mainly analyzed based on human cognitive abilities, which can take considerable time and can lead to errors due to misunderstanding. While efforts have been made to automate this process, that the existing academic efforts to automate the process have limitations. To develop an automated specification reviewing model applicable to various kinds of

In order to achieve database-free (DB-free) vision-based monitoring on construction sites, this paper proposes a deep active learning approach that automatically evaluates the uncertainty of unlabeled training data, selects the most meaningful-to-learn instances, and eventually trains a deep learning model with the selected data. The proposed approach thus involves three sequential processes: (1) uncertainty evaluation of unlabeled data, (2) training data sampling and user-interactive labeling,

Existing attempts to automate construction document analysis are limited in understanding the varied semantic properties of different documents. Due to the semantic conflicts, the construction specification review process is still conducted manually in practice despite the promising performance of the existing approaches. This research aimed to develop an automated system for reviewing construction specifications by analyzing the different semantic properties using natural language processing te

Many city governments have implemented promising smart initiatives to make cities more efficient, livable, and ecological. To harness the full potential of smart city initiatives, it is vital for policymakers to align citizens with the project objectives. This study comprehensively characterizes and classifies top-down announcements formulated by city developers into six alignment categories (i.e., smart economy, smart people, smart governance, smart mobility, smart environment, and smart living